A New And Fast Rival Genetic Algorithm For Feature Selection

Too, Jing Wei and Abdullah, Abdul Rahim (2021) A New And Fast Rival Genetic Algorithm For Feature Selection. Journal of Supercomputing, 77 (3). pp. 2844-2874. ISSN 0920-8542

Text 2021 A_NEW_AND_FAST_RIVAL_GENETIC_ALGORITHM_FOR_FEATURE.PDF Restricted to Registered users only Download (2MB)

Abstract

Feature selection is one of the significant steps in classification tasks. It is a pre-processing step to select a small subset of significant features that can contribute the most to the classification process. Presently, many metaheuristic optimization algorithms were successfully applied for feature selection. The genetic algorithm (GA) as a fundamental optimization tool has been widely used in feature selection tasks. However, GA suffers from the hyperparameter setting, high computational complexity, and the randomness of selection operation. Therefore, we propose a new rival genetic algorithm, as well as a fast version of rival genetic algorithm, to enhance the performance of GA in feature selection. The proposed approaches utilize the competition strategy that combines the new selection and crossover schemes, which aim to improve the global search capability. Moreover, a dynamic mutation rate is proposed to enhance the search behaviour of the algorithm in the mutation process. The proposed approaches are validated on 23 benchmark datasets collected from the UCI machine learning repository and Arizona State University. In comparison with other competitors, proposed approach can provide highly competing results and overtake other algorithms in feature selection.

Item Type:	Article
Uncontrolled Keywords:	Binary optimization, Classification, Competition strategy, Feature selection, Genetic algorithm, Wrapper approach
Divisions:	Faculty of Electrical Engineering
Depositing User:	Sabariah Ismail
Date Deposited:	08 Mar 2022 11:43
Last Modified:	08 Mar 2022 11:43
URI:	http://eprints.utem.edu.my/id/eprint/25628
Statistic Details:	View Download Statistic

Actions (login required)

View Item